Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Jung-Woo Chang
Network and Distributed System Security (NDSS) Symposium 2025 · Day 1 · Wireless, Cellular & Satellite Security
The integration of machine learning (ML) into wireless communication systems, particularly for the nascent **AI-native 6G** networks, promises unprecedented efficiency and adaptability. However, this transformative shift introduces novel security vulnerabilities, specifically in the physical layer, which have been largely unaddressed in a comprehensive manner. Jung-Woo Chang's presentation on Magmaw introduces a groundbreaking framework for **modality-agnostic adversarial attacks** designed to compromise these next-generation ML-driven wireless systems. This research, a collaboration between UC San Diego and KDDR Research, highlights a critical new threat surface that could undermine the reliability and integrity of future wireless infrastructure.
AI review
Solid, original research that advances a real and underexplored threat surface: adversarial attacks on ML-based physical-layer wireless systems. The modality-agnostic framing is a genuine contribution over prior single-modality work, the threat model is grounded, and the hardware validation with USRP in a real lab environment gives this teeth.